R. C.
Atkinson and R. M. Shiffrin.
Human memory: a proposed system and its control processes.
In K. W. Spence, editor, Psychology of learning and motivation: advances in
research and theory, volume 2, pages 89-195. Academic Press, New York,
1968.

N. Ayache and O. D.
Faugeras.
Hyper: a new approach for the recognition and positioning of two-dimensional
objects.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
PAMI-8(1):44-54, 1986.

S. Bentin and
L. B. Feldman.
The contribution of morphological and semantic relatedness to repetition
priming at short and long time lags: Evidence from hebrew.
Q. Journal Exp. Psychol., 42A:693-711, 1990.

S. Edelman,
D. Reisfeld, and Y. Yeshurun.
A system for face recognition that learns from examples.
CS-TR 91-20, Weizmann Institute of Science, October 1991.
to appear in Proc. 2nd European Conf. on Computer Vision.

M. Gasser.
Transfer in a connectionist model of the acquisition of morphology.
CogSci TR 147, Indiana University, Bloomington, IN, 1995.
an expanded version of a paper presented at the Morphology Workshop, Nijmegen,
June 13, 1995.

There is evidence that the specialized neural
processing systems in the neocortex, which are responsible for much of human
cognition, arise from the action of a relatively general-purpose learning
mechanism. I propose that such a neocortical learning mechanism can be best
understood as the combination of error-driven and self-organizing (Hebbian
associative) learning. This model of neocortical learning, called LEABRA
(local, error-driven and associative, biologically realistic algorithm), is
computationally powerful, has important implications for psychological
models, and is biologically feasible. The thesis begins with an evaluation of
the strengths and limitations of current neural network learning algorithms
as models of a neocortical learning mechanism according to psychological,
biological, and computational criteria. I argue that error-driven (e.g.,
backpropagation) learning is a reasonable computational and psychological
model, but it is biologically implausible. I show that backpropagation can be
implemented in a biologically plausible fashion by using interactive
(bi-directional, recurrent) activation flow, which is known to exist in the
neocortex, and has been important for accounting for psychological data.
However, the interactivity required for biological and psychological
plausibility significantly impairs the ability to respond systematically to
novel stimuli, making it still a bad psychological model (e.g., for nonword
reading). I propose that the neocortex solves this problem by using
inhibitory activity regulation and Hebbian associative learning, the
computational properties of which have been explored in the context of
self-organizing learning models. I show that by introducing these properties
into an interactive (biologically plausible) error-driven network, one
obtains a model of neocortical learning that: 1) provides a clear
computational role for a number of biological features of the neocortex; 2)
behaves systematically on novel stimuli, and exhibits transfer to novel
tasks; 3) learns rapidly in networks with many hidden layers; 4) provides
flexible access to learned knowledge; 5) shows promise in accounting for
psychological phenomena such as the U-shaped curve in over-regularization of
the past-tense inflection; 6) has a number of

P. Rives,
B. Bouthemy, B. Prasada, and E. Dubois.
Recovering the orientation and the position of a rigid body in space from a
single view.
Technical report, INRS-Telecommunications, Quebec, Canada, 1981.

K. Sims.
Interactive evolution of dynamical systems.
In Toward a Practice of Autonomous Systems: Proceedings of the First
European Conference on Artificial Life, pages 171-178, Paris, December
1991. MIT Press.

K. Tanaka, Y. Fukada, and H. Saito.
Underlying mechanisms of the response specificity of expansion/contraction and
rotation cells in the dorsal part of the medial superior temporal area of the
Macaque monkey.
J. Neurophysiology, 62:642-656, 1989.

R. C.
Atkinson and R. M. Shiffrin.
Human memory: a proposed system and its control processes.
In K. W. Spence, editor, Psychology of learning and motivation: advances in
research and theory, volume 2, pages 89-195. Academic Press, New York,
1968.

N. Ayache and O. D.
Faugeras.
Hyper: a new approach for the recognition and positioning of two-dimensional
objects.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
PAMI-8(1):44-54, 1986.

S. Bentin and
L. B. Feldman.
The contribution of morphological and semantic relatedness to repetition
priming at short and long time lags: Evidence from hebrew.
Q. Journal Exp. Psychol., 42A:693-711, 1990.

S. Edelman,
D. Reisfeld, and Y. Yeshurun.
A system for face recognition that learns from examples.
CS-TR 91-20, Weizmann Institute of Science, October 1991.
to appear in Proc. 2nd European Conf. on Computer Vision.

M. Gasser.
Transfer in a connectionist model of the acquisition of morphology.
CogSci TR 147, Indiana University, Bloomington, IN, 1995.
an expanded version of a paper presented at the Morphology Workshop, Nijmegen,
June 13, 1995.

P. Rives,
B. Bouthemy, B. Prasada, and E. Dubois.
Recovering the orientation and the position of a rigid body in space from a
single view.
Technical report, INRS-Telecommunications, Quebec, Canada, 1981.

I. Rock,
J. DiVita, and R. Barbeito.
The effect on form perception of change of orientation in the third dimension.
Journal of Experimental Psychology: Human Perception and Performance,
7:719-732, 1981.

E. T. Rolls and
M. J. Tovee.
The responses of single neurons in the temporal visual cortical areas of the
macaque when more than one stimulus is present in the receptive field.
Exp. Brain Res., 103:409-420, 1995.

K. Sims.
Interactive evolution of dynamical systems.
In Toward a Practice of Autonomous Systems: Proceedings of the First
European Conference on Artificial Life, pages 171-178, Paris, December
1991. MIT Press.

K. Tanaka, Y. Fukada, and H. Saito.
Underlying mechanisms of the response specificity of expansion/contraction and
rotation cells in the dorsal part of the medial superior temporal area of the
Macaque monkey.
J. Neurophysiology, 62:642-656, 1989.

M. J. Tovee, E. T. Rolls, and P. Azzopardi.
Translation invariance in the responses to faces of single neurons in the
temporal visual cortical areas of the alert monkey.
J. of Neurophysiology, 72:1049-1060, 1994.